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How Physics AI Is Transforming Simulations.
Physics AI: Merging Machine Learning with Simulation

The frontier of engineering simulation is rapidly evolving as Physics AI – machine learning trained on physics data – brings near–real-time analysis to once time‑intensive workflows. Instead of running a single simulation at a time, Physics AI learns from hundreds of past finite element (FEA/CFD/etc.) analyses and then predicts outcomes for new designs almost instantly. In effect, it maps geometry to performance through modern geometric deep learning, enabling designers to iterate faster. Once trained on existing simulation data, a Physics AI model can output full animated physics results orders of magnitude faster than a conventional solver
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In practice, engineers simply feed their CAD/mesh into the trained model and get instant contours or force plots. For example, Altair’s PhysicsAI demo showed a team importing a 3D HVAC duct model and clicking “Predict” – the AI returned a detailed pressure map in ~3 seconds
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, compared to minutes or hours with a CFD solver. In another case, a head‑impact crash test was predicted in full animation by the AI
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. These breakthroughs – achieved by geometric deep nets that work directly on meshes – slash runtimes from days or months to mere seconds
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By blending AI with physics, Physics AI doesn’t replace traditional solvers; rather, it augments them. Tens to thousands of solver runs (FEA, CFD, etc.) are first used as training data
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. The model then generalizes to new designs, essentially serving as a super‑fast surrogate for the solver. The result is an “accelerated design cycle”: teams can explore many more “what-if” scenarios early on, catching problems in the digital lab long before hardware prototypes. (As one Altair expert notes, geometric deep learning for simulation could spawn foundational AI models akin to language models – but built on geometry and physics.

Medium: https://jhparmar.medium.com/how-physi...